IoT-Driven Information Acquisition and Processing Architecture for Real-Time Systems

Abstract

Through the use of various information and communication technologies (ICT), connected and autonomous cars have the potential to improve traffic safety. In order to model these signs, conventional traffic elements like driver, vehicle, road, and surroundings must be taken into account. In the end, traffic safety indications can be shown to drivers in an appropriate aggregate form to draw their attention and persuade them to make choices that will prevent and lessen traffic accidents. Massive amounts of real-time data have been produced as a result of the Internet of Things' (IoT) rapid expansion, necessitating effective data collection, processing, and analysis. The latency, scalability, as well as reliability necessities for real-time applications are difficult for traditional centralised architectures to achieve. In order to support real-time systems, this work presents an IoT-driven information gathering and processing architecture that integrates cloud-based analytics, edge computing, and sensor networks. The suggested design guarantees scalability as well as data reliability while enabling low-latency data collecting, instantaneous processing and intelligent decision-making. The design is appropriate for time-sensitive applications including smart medical care, industrial automation, and smart transportation systems since experimental evaluation shows improved response time, decreased network overhead, and increased system performance.